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Efficient trajectory planning for urban intersections is currently one of themost challenging tasks for an Autonomous Vehicle (AV). Courteous behaviortowards other traffic participants, the AV's comfort and its progression in theenvironment are the key aspects that determine the performance of trajectoryplanning algorithms. To capture these aspects, we propose a novel trajectoryplanning framework that ensures social compliance and simultaneously optimizesthe AV's comfort subject to kinematic constraints. The framework combines alocal continuous optimization approach and an efficient driver model to ensurefast behavior prediction, maneuver generation and decision making over longhorizons. The proposed framework is evaluated in different scenarios todemonstrate its capabilities in terms of the resulting trajectories andruntime.

Trajectory Planning for Automated Driving in Intersection Scenarios using Driver Models